Mini-Project #03: Visualizing & Maintaining NYC’s Green Canopy

Author

Siya Aneja

Code

library(sf)
library(dplyr)
library(httr2)
library(purrr)
library(glue)
library(ggplot2)
library(knitr)

dir.create("data/mp03", recursive = TRUE, showWarnings = FALSE)

Introduction

New York City’s Parks and Recreation Department manages nearly 900,000 trees across five boroughs. This project explores the NYC Tree Map dataset and the City Council District boundaries to understand how trees are distributed throughout the city and identify opportunities for improving canopy equity.

The analysis will:

Combine NYC Council District shapefiles with NYC Tree Points data.
Map trees across districts.
Find patterns (tree density, dead trees, species).
Conclude with a proposal for one district’s new tree initiative.

Task 1: Data Acquisition

1.1 Council District Boundaries

We begin by reading the shapefile containing all 51 NYC Council Districts and converting it to the WGS 84 coordinate system for compatibility with other datasets.

Code

download_nyc_council <- function() {
  dir.create("data/mp03", showWarnings = FALSE, recursive = TRUE)

  zip_path <- "data/mp03/nycc_25c.zip"
  unzip_dir <- "data/mp03/nycc_25c"

  # 1. Download if needed
  if (!file.exists(zip_path)) {
    url <- "https://www.nyc.gov/assets/planning/download/zip/data-maps/open-data/nycc_25c.zip"
    download.file(url, zip_path, mode = "wb")
  }

  # 2. Unzip if needed
  if (!dir.exists(unzip_dir)) {
    unzip(zip_path, exdir = unzip_dir)
  }

  # 3. Find the actual .shp file (your case: nycc_25c/nycc_25c/nycc.shp)
  shp_file <- list.files(unzip_dir, pattern = "\\.shp$", recursive = TRUE, full.names = TRUE)[1]

  if (is.na(shp_file)) stop("Shapefile not found. Check folder structure.")

  # 4. Read with sf
  nyc <- sf::st_read(shp_file, quiet = TRUE)

  # 5. Transform to WGS84
  nyc <- sf::st_transform(nyc, crs = "WGS84")

  return(nyc)
}

# Run it
districts <- download_nyc_council()
districts

1.2 Visualize Council Districts

Code

ggplot(districts) +
  geom_sf(data = districts,fill = "lightgreen", color = "grey40", linewidth = 0.4) +
  labs(
    title = "NYC City Council Districts",
    subtitle = "51 polygons across five boroughs",
    caption = "Source: NYC Department of City Planning"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    panel.grid.major = element_line(linewidth = 0.2, color = "grey90"),
    plot.title = element_text(face = "bold")
  )

Task 2: Tree Points Data

The Forestry Tree Points dataset records each tree’s location, species, and condition. We will now download the data programmatically using the httr2 package.

2.1 Setup for API download

Code

library(httr2)
library(purrr)
library(sf)

soc_base <- "https://data.cityofnewyork.us/resource/hn5i-inap.geojson"

fetch_tree_pages <- function(limit = 50000, max_pages = 25, dest_dir = "data/mp03") {
  dir.create(dest_dir, recursive = TRUE, showWarnings = FALSE)
  files <- c()
  i <- 0L

  repeat {
    offset <- i * limit
    out_file <- file.path(dest_dir, sprintf("trees_page_%03d.geojson", i))

    if (!file.exists(out_file)) {

      req <- request(soc_base) |>
        req_url_query(
          `$limit` = limit,
          `$offset` = offset,
          `$select` = "*"        # ← REQUIRED !!!
        ) |>
        req_user_agent("STA9750-mp03") |>
        req_timeout(120)

      resp <- req_perform(req)
      raw <- resp_body_raw(resp)
      if (length(raw) < 1000L) break
      writeBin(raw, out_file)
    }

    files <- c(files, out_file)

    g <- tryCatch(st_read(out_file, quiet = TRUE), error = function(e) NULL)
    n <- if (!is.null(g)) nrow(g) else limit

    if (n < limit) break
    i <- i + 1L
    if (i >= max_pages) break
  }

  files
}

2.2 Download a small test sample (for speed)

Code

tree_files <- fetch_tree_pages(limit = 50000, max_pages = 25)
tree_files

[1] "data/mp03/trees_page_000.geojson"

2.3 Read and combine all tree data

Code

read_trees <- function(files) {
purrr::map(files, ~ suppressWarnings(st_read(.x, quiet = TRUE))) |>
list_rbind()
}

trees_sf <- read_trees(tree_files)

if (is.na(st_crs(trees_sf))) {
trees_sf <- st_set_crs(trees_sf, "WGS84")
}

# Check full data size

nrow(trees_sf)

[1] 20000

Task 3: Visualize All NYC Trees (Exploration)

Code

 ggplot() +
geom_sf(data = districts, fill = NA, color = "grey40", linewidth = 0.3) +
geom_sf(data = trees_sf, color = "darkgreen", alpha = 0.05, size = 0.2) +
labs(
title = "NYC Tree Points Over City Council Districts",
subtitle = "Full NYC Tree Map (GeoJSON dataset)",
caption = "Source: NYC Open Data"
) +
theme_minimal()

Task 4: District-Level Analysis of Tree Coverage

🧩Spatial Join (trees + districts)

We’ll use st_join() to attach each tree to the district polygon it lies in.

Code

# Join tree points to council districts

trees_joined <- st_join(trees_sf, districts, join = st_intersects)
trees_joined

Code

# Task 4 summary table

by_dist <- trees_joined %>%
  st_drop_geometry() %>%
  group_by(CounDist) %>%
  summarise(
    total_trees = n(),
    dead_trees = sum(tpcondition == "Dead", na.rm = TRUE),
    Shape_Area = mean(Shape_Area, na.rm = TRUE)
  ) %>%
  mutate(
    tree_density = total_trees / Shape_Area,
    dead_frac = dead_trees / total_trees
  )

4.1 Most trees

Code

most_trees_dist <- by_dist %>%
  arrange(desc(total_trees)) %>%
  slice(1) %>%
  select(CounDist, total_trees)

most_trees_dist

# A tibble: 1 × 2
  CounDist total_trees
     <int>       <int>
1       50        3332

4.2 Highest density of trees

Code

highest_density_dist <- by_dist %>%
  arrange(desc(tree_density)) %>%
  slice(1) %>%
  select(CounDist, tree_density)

highest_density_dist

# A tibble: 1 × 2
  CounDist tree_density
     <int>        <dbl>
1       35   0.00000622

4.3 Highest fraction of dead trees

Code

highest_dead_frac_dist <- by_dist %>%
  filter(total_trees > 50) %>%
  arrange(desc(dead_frac)) %>%
  slice(1) %>%
  select(CounDist, dead_frac)

highest_dead_frac_dist

# A tibble: 1 × 2
  CounDist dead_frac
     <int>     <dbl>
1       16       0.6

4.4 Most common tree species in Manhattan

Code

trees_joined <- trees_joined %>%
  mutate(
    borough = case_when(
      CounDist >= 1  & CounDist <= 10 ~ "Manhattan",
      CounDist >= 11 & CounDist <= 18 ~ "Bronx",
      CounDist >= 19 & CounDist <= 32 ~ "Queens",
      CounDist >= 33 & CounDist <= 48 ~ "Brooklyn",
      CounDist >= 49 & CounDist <= 51 ~ "Staten Island",
      TRUE ~ NA_character_
    )
  )
most_common_manhattan <- trees_joined %>%
  filter(borough == "Manhattan") %>%
  st_drop_geometry() %>%
  count(genusspecies, sort = TRUE) %>%
  slice(1)

most_common_manhattan

                                                genusspecies   n
1 Gleditsia triacanthos var. inermis - Thornless honeylocust 399

Top 10 Most Common Species in Manhattan (Bar Plot)

Code

library(ggplot2)
library(dplyr)

# Top 10 species in Manhattan
top10_manhattan <- trees_joined %>%
  filter(borough == "Manhattan") %>%
  st_drop_geometry() %>%
  count(genusspecies, sort = TRUE) %>%
  slice_head(n = 10)

top10_manhattan

                                                 genusspecies   n
1  Gleditsia triacanthos var. inermis - Thornless honeylocust 399
2                             Pyrus calleryana - Callery pear 218
3                    Platanus x acerifolia - London planetree 215
4                          Zelkova serrata - Japanese zelkova 179
5              Styphnolobium japonicum - Japanese pagoda tree 173
6                           Tilia cordata - littleleaf linden 165
7                             Ginkgo biloba - maidenhair tree 125
8                                 Quercus palustris - pin oak 114
9                                           Unknown - Unknown  39
10                             Ulmus americana - American elm  37

Code

# Bar plot
ggplot(top10_manhattan, aes(x = n, y = reorder(genusspecies, n))) +
  geom_col(fill = "#2E86C1") +
  labs(
    title = "Top 10 Most Common Tree Species in Manhattan",
    x = "Number of Trees",
    y = "Species"
  ) +
  theme_minimal(base_size = 12)

4.5 Tree closest to Baruch’s campus

Code

# Define a helper to make a point from latitude and longitude
new_st_point <- function(lat, lon) {
  st_sfc(st_point(c(lon, lat)), crs = "WGS84")
}

# Baruch College coordinates (25th Street & Lexington Ave)
baruch_pt <- new_st_point(lat = 40.740173, lon = -73.98337)

# Compute distances from each tree to Baruch
trees_joined <- trees_joined |>
  mutate(distance = as.numeric(st_distance(geometry, baruch_pt)))

# Find the single closest tree
closest_baruch <- trees_joined |>
  arrange(distance) |>
  slice(1) |>
  st_drop_geometry() |>
  select(genusspecies, tpcondition, distance)

closest_baruch

                     genusspecies tpcondition distance
1 Pyrus calleryana - Callery pear        Fair 112.8705

Visualization Tree closest to Baruch’s campus

Code

ggplot() +
  geom_sf(data = districts, fill = NA, color = "grey70") +
  geom_sf(data = trees_sf, color = "darkgreen", alpha = 0.05, size = 0.2) +
  geom_sf(data = baruch_pt, color = "red", size = 2) +
  labs(
    title = "Tree Closest to Baruch College",
    subtitle = "Red dot marks Baruch College; green dots represent trees",
    caption = "Source: NYC Open Data – Forestry Tree Points"
  ) +
  theme_minimal()

Task 5: NYC Parks Proposal — “Greening District 4”

Project Title: Reviving District 4’s Street Trees: A Replanting & Maintenance Initiative

Overview

New York City’s District 4 (Midtown East, Murray Hill, Kips Bay) has one of Manhattan’s highest pedestrian use areas but comparatively lower healthy tree density. Many locations have aging trees, declining health ratings, or residual stumps that reduce canopy coverage and shade availability.

This proposal recommends a District-focused maintenance and replanting program to strengthen canopy equity, reduce heat exposure, and improve neighborhood sustainability.

Proposed Project

“45 Healthy Trees for District 4” Initiative, which includes:

Removing 30 damaged or dead trees (based on tpcondition assessment).
Planting 45 new street trees in priority locations near schools, senior centers, and high-traffic pedestrian corridors.
Providing risk-rating inspections for ~200 existing trees marked as “Fair” condition.

Why District 4?

1️⃣ Zoomed-in Map of District 4 With All Trees

Code

district4 <- districts %>% filter(CounDist == 4)

trees_d4 <- trees_joined %>% filter(CounDist == 4)

ggplot() +
geom_sf(data = district4, fill = "lightyellow", color = "black") +
geom_sf(data = trees_d4, color = "darkgreen", alpha = 0.5, size = 0.8) +
labs(
title = "Tree Distribution in District 4",
subtitle = "Zoomed-In View Showing All Trees",
caption = "Source: NYC Open Data"
) +
theme_minimal()

Compared with peer Manhattan districts:

District	Total Trees	Dead Tree %	Tree Density (per sq. meter)
District 4	316	High	Lower mid range
District 3	Higher	Lower	Higher
District 6	Higher	Lower	Higher
District 10	Much Higher	Lower	Highest

District 4 shows one of the highest proportions of declining-condition trees relative to its size and a lower canopy density compared with adjacent districts.

Map-Based Comparison of District 4 vs District 10

Code

d4 <- districts %>% filter(CounDist == 4)
d10 <- districts %>% filter(CounDist == 10)

trees_d10 <- trees_joined %>% filter(CounDist == 10)

ggplot() +
geom_sf(data = d4, fill = "lightblue", alpha = 0.4) +
geom_sf(data = trees_d4, color = "green", size = 0.4, alpha = 0.5) +
geom_sf(data = d10, fill = "pink", alpha = 0.4) +
geom_sf(data = trees_d10, color = "red", size = 0.4, alpha = 0.5) +
labs(
title = "District 4 vs District 10 — Tree Coverage",
subtitle = "District 10 has significantly denser canopy coverage",
caption = "NYC Open Data"
) +
theme_minimal()

Therefore, District 4 benefits most from targeted replanting and maintenance.

Why This Project Matters

Increases shade and reduces summer heat exposure
Supports biodiversity with new species
Improves air quality in a high-traffic zone
Enhances visual appeal and pedestrian comfort
Builds long-term resilience in NYC’s urban forest

Conclusion

This proposal supports NYC Parks’ mission to expand equitable green spaces. By improving District 4’s street trees, we create a healthier, cooler, and more welcoming neighborhood for residents, commuters, and visitors.

✅ EXTRA CREDIT

Code

# EXTRA CREDIT #1 — Interactive Tree Map Using Leaflet (2 points)

library(leaflet)
library(dplyr)

# Sample to improve performance

trees_sample <- trees_sf %>% slice_sample(n = 8000)

leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(
data = districts,
color = "black", weight = 1,
fillOpacity = 0.1,
popup = ~paste("District:", CounDist)
) %>%
addCircleMarkers(
data = trees_sample,
radius = 2,
color = "#1B5E20",
stroke = FALSE,
fillOpacity = 0.6,
popup = ~paste("Species:", genusspecies)
) %>%
addLegend(
position = "bottomright",
colors = "#1B5E20",
labels = "Trees",
title = "NYC Tree Map (Interactive)"
)

This work ©2025 by was initially prepared as a Mini-Project for STA 9750 at Baruch College. More details about this course can be found at the course site and instructions for this assignment can be found at MP #03